Suppose you want to predict whether tomorrow will be a sunny or rainy day. You can develop an algorithm that is based on the current weather and your meteorological knowledge using a rather complicated set of rules to return the desired prediction. Now suppose that you have a record of the day-by-day weather conditions for the last five years, and you find that every time you had two sunny days in a row, the following day also happened to be a sunny one. Your algorithm could generalize this and predict that tomorrow will be a sunny day since the sun reigned today and yesterday. This algorithm is a pretty simple example of learning from experience. This is what Machine Learning is all about: algorithms that learn from the available data.
This course is designed in the same way that many data science and analytics projects play out. First, we need to acquire data; the data is often messy, incomplete, or not correct in some way. Therefore, we spend the first chapter talking about strategies for dealing with bad data and ways to deal with other problems that arise from data. For example, what happens if we have too many features? How do we handle that?
Module 1, Learning scikit-learn: Machine Learning in Python, in this module, you will learn several methods for building Machine Learning applications that solve different real-world tasks, from document classification to image recognition. We will use Python, a simple, popular, and widely used programming language, and scikit-learn, an open source Machine Learning library. In each chapter of this module, we will present a different Machine Learning setting and a couple of well-studied methods as well as show step-by-step examples that use Python and scikit-learn to solve concrete tasks. We will also show you tips and tricks to improve algorithm performance, both from the accuracy and computational cost point of views.
Module 2, scikit-learn Cookbook, the first chapter of this module is your guide. The meat of this module will walk you through various algorithms and how to implement them into your workflow. And finally, we'll end with the postmodel workflow. This chapter is fairly agnostic to the other chapters of the module and can be applied to the various algorithms you'll learn up until the final chapter.
Module 3, Mastering Machine Learning with scikit-learn, in this module, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly applied to, and learn to measure the performance of machine learning systems. We will work with a popular library for the Python programming language called scikit-learn, which has assembled excellent implementations of many machine learning models and algorithms under a simple yet versatile API.
This module is motivated by two goals:
- Its content should be accessible. The book only assumes familiarity with basic programming and math.
- Its content should be practical. This book offers hands-on examples that readers can adapt to problems in the real world.